Purpose Myelofibrosis (MF) is the most severe myeloproliferative neoplasm (MPN) where there remains a need for improved risk stratification methods to better inform patient management. Since MF is a stem cell driven disease and stem cell informed transcriptomic information has been shown to be prognostic across other clinical settings we sought to use this information to generate novel transcriptomic-based risk stratification models that could complement current approaches.

Patients and Methods We identified 358 MF patients from the MPN registry at the Princess Margaret Cancer Centre (ClinicalTrials.gov Identifier: NCT02760238) from whom peripheral blood mononuclear cells were collected and clinical data was available. We randomly split our cohort into a 250-patient training set and a 108-patient test set to train and validate prognostic models, respectively.

Results Within the training set we used repeated nested cross validation together with LASSO regression from various starting gene sets and found that the best prognostic models were consistently derived from transcriptomic variation among MF stem cells. From this gene set we trained our final model, a 24-gene weighted expression score (termed, MPN24) that is prognostic for overall survival. Patients were classified as MPN24-High or MPN24-Low risk depending on whether their scores were above or below the within cohort median defined in the training set. The prognostic power of MPN24 was validated in the test set patients with stark differences in survival outcomes for MPN24-High (5-year survival rate = 21% [95% CI 9%-52%]) and MPN24-Low risk patients (5-year survival rate = 71% [95% CI 57-88%]) patients, resulting in a HR of 5.3 (95% CI: 2.6-10.5; p=2.08e-6). MPN24 captures unique prognostic information to current risk stratification models such as DIPSS, MIPSS70 and the Genomic-Personalized Risk scores. Therefore, we present a novel 3-tier risk stratification approach that integrates DIPSS and MPN24 to more effectively risk stratify MF patients, particularly via up or downscaling patient risk within the DIPSS-Intermediate-1/2 categories. In this integrated model patients were classified as Integrated-Low, Integrated-Intermediate or Integrated-High, and experienced 5-year survival rates of 88.2% [95% CI 77.9% - 99.9%], 39.3% [95% CI 19.9% - 77.7%], and 10.8% [95% CI 2.1% - 55.8%], respectively (likelihood ratio test p = 1e-8). Finally, from MPN24 genes we derived a 13-gene subsignature (termed, MPN13) from the training set patients that was validated to predict time-to-transformation in the test set patients when classified as MPN13-High or Low relative to the 80th percentile of MPN13 scores from the training set (p=0.0047). In the test set, MPN13-High and MPN13-Low patients experienced 3-year cumulative incidences of transformation of 5.2% [95% CI 0.2%-10.2%] and 28.6% [95% CI 3.1%-54.0%] respectively, after adjusting for death as a competing risk.

Conclusions Transcriptomic information informed by MF stem cells offer novel and unique prognostic potential in MF that significantly complements current approaches. Future work will be needed to validate the robustness of the approach in external cohorts and identify how patient management can be optimized with these novel transcriptomic biomarkers.

VG reports consultancy with AbbVie, Bristol Myers Squibb/Celgene, Novartis, Pfizer, Daiichi Sankyo, GSK, Incyte; data safety or advisory board participation with AbbVie, Bristol Myers Squibb/Celgene, GSK, Incyte; honoraria from Bristol Myers Squibb/Celgene, Novartis; research support from Novartis, AbbVie. JED has licensing agreements with trillium Therapeutics and Pfizer for SIRP-a and has a sponsored research agreement with BMS.

Program project grant to VG from The Elizabeth and Tony Comper Foundation; VG is supported by Barbara Baker Chair Award for Leukemia and related disorders; JJFM is the recipient of the Canadian Institutes of Health Research Doctoral Award: Frederick Banting and Charles Best Canada Graduate Scholarships [FBD-170928].

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Research Ethics Board of University Health Network gave ethical approval of this work (REB #17-5601).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data produced in the present study are available upon reasonable request to the authors.